Adaptively weighted maximum likelihood estimation of discrete distributions

SummaryDiscrete data arise in various research fields, typically when the observations are count data.I propose a robust and efficient parametric procedure for estimation of discrete distributions. The estimation is done in two phases. First, a very robust, but possibly inefficient, estimate of the model parameters is computed and used to indentify outliers. Then the outliers are either removed from the sample or given low weights, and a weighted maximum likelihood estimate (WML) is computed.The weights are determined via an adaptive process such that if the data follow the model, then asymptotically no observation is downweighted.I prove that the final estimator inherits the breakdown point of the initial one, and that its influence function at the model is the same as the influence function of the maximum likelihood estimator, which strongly suggests that it is asymptotically fully efficient.The initial estimator is a minimum disparity estimator (MDE). MDEs can be shown to have full asymptotic efficiency, and some MDEs have very high breakdown points and very low bias under contamination. Several initial estimators are considered, and the performances of the WMLs based on each of them are studied.It results that in a great variety of situations the WML substantially improves the initial estimator, both in terms of finite sample mean square error and in terms of bias under contamination. Besides, the performances of the WML are rather stable under a change of the MDE even if the MDEs have very different behaviors.Two examples of application of the WML to real data are considered. In both of them, the necessity for a robust estimator is clear: the maximum likelihood estimator is badly corrupted by the presence of a few outliers.This procedure is particularly natural in the discrete distribution setting, but could be extended to the continuous case, for which a possible procedure is sketched.RésuméLes données discrètes sont présentes dans différents domaines de recherche, en particulier lorsque les observations sont des comptages.Je propose une méthode paramétrique robuste et efficace pour l'estimation de distributions discrètes. L'estimation est faite en deux phases. Tout d'abord, un estimateur très robuste des paramètres du modèle est calculé, et utilisé pour la détection des données aberrantes (outliers). Cet estimateur n'est pas nécessairement efficace. Ensuite, soit les outliers sont retirés de l'échantillon, soit des faibles poids leur sont attribués, et un estimateur du maximum de vraisemblance pondéré (WML) est calculé.Les poids sont déterminés via un processus adaptif, tel qu'asymptotiquement, si les données suivent le modèle, aucune observation n'est dépondérée.Je prouve que le point de rupture de l'estimateur final est au moins aussi élevé que celui de l'estimateur initial, et que sa fonction d'influence au modèle est la même que celle du maximum de vraisemblance, ce qui suggère que cet estimateur est pleinement efficace asymptotiquement.L'estimateur initial est un estimateur de disparité minimale (MDE). Les MDE sont asymptotiquement pleinement efficaces, et certains d'entre eux ont un point de rupture très élevé et un très faible biais sous contamination. J'étudie les performances du WML basé sur différents MDEs.Le résultat est que dans une grande variété de situations le WML améliore largement les performances de l'estimateur initial, autant en terme du carré moyen de l'erreur que du biais sous contamination. De plus, les performances du WML restent assez stables lorsqu'on change l'estimateur initial, même si les différents MDEs ont des comportements très différents.Je considère deux exemples d'application du WML à des données réelles, où la nécessité d'un estimateur robuste est manifeste : l'estimateur du maximum de vraisemblance est fortement corrompu par la présence de quelques outliers.La méthode proposée est particulièrement naturelle dans le cadre des distributions discrètes, mais pourrait être étendue au cas continu.

Prise en charge nutritionnelle préopératoire. [Preoperative nutritional support.]

Undernutrition is an independent factor of postoperative morbidity and mortality The aim of a preoperative nutritional support is to enhance immune muscular and cognitive functions, and to support wound healing This nutritional support (e g dietary management enteral or parenteral nutrition) should be limited to high risk situations with a beneficial effect of nutrition for the patient undernutrition major surgery and elderly Preoperative nutritional support should be scheduled for atleast 7 to 10 days before the surgery During the preoperative period the type and route of an eventual postoperative nutritional assistance should be anticipated In the case of emergency surgery nutritional assessment of the patient should be done as soon as possible before surgery or in the 48 h postoperative period Finally, in elective surgery, preoperative fasting should be limited to 2-3 hours for clear liquids and 6 hours for solids (C) 2010 Published by Elsevier Masson SAS

Support for Seasonal Influenza Vaccination Requirements among US Healthcare Personnel.

Objective. To measure support for seasonal influenza vaccination requirements among US healthcare personnel (HCP) and its associations with attitudes regarding influenza and influenza vaccination and self-reported coverage by existing vaccination requirements. Design. Between June 1 and June 30, 2010, we surveyed a sample of US HCP ([Formula: see text]) recruited using an existing probability-based online research panel of participants representing the US general population as a sampling frame. Setting. General community. Participants. Eligible HCP who (1) reported having worked as medical doctors, health technologists, healthcare support staff, or other health practitioners or who (2) reported having worked in hospitals, ambulatory care facilities, long-term care facilities, or other health-related settings. Methods. We analyzed support for seasonal influenza vaccination requirements for HCP using proportion estimation and multivariable probit models. Results. A total of 57.4% (95% confidence interval, 53.3%-61.5%) of US HCP agreed that HCP should be required to be vaccinated for seasonal influenza. Support for mandatory vaccination was statistically significantly higher among HCP who were subject to employer-based influenza vaccination requirements, who considered influenza to be a serious disease, and who agreed that influenza vaccine was safe and effective. Conclusions. A majority of HCP support influenza vaccination requirements. Moreover, providing HCP with information about the safety of influenza vaccination and communicating that immunization of HCP is a patient safety issue may be important for generating staff support for influenza vaccination requirements.

Neurally Adjusted Ventilatory Assist (NAVA) improves the matching of diaphragmatic electrical activity and tidal volume in comparison to pressure support (PS) under non invasive ventilation

INTRODUCTION. Patient-ventilator asynchrony is a frequent issue in non invasivemechanical ventilation (NIV) and leaks at the patient-mask interface play a major role in itspathogenesis. NIV algorithms alleviate the deleterious impact of leaks and improve patient-ventilator interaction. Neurally adusted ventilatory assist (NAVA), a neurally triggered modethat avoids interferences between leaks and the usual pneumatic trigger, could further improvepatient-ventilator interaction in NIV patients.OBJECTIVES. To evaluate the feasibility ofNAVAin patients receiving a prophylactic postextubationNIV and to compare the respective impact ofPSVandNAVAwith and withoutNIValgorithm on patient-ventilator interaction.METHODS. Prospective study conducted in 16 beds adult critical care unit (ICU) in a tertiaryuniversity hospital. Over a 2 months period, were included 17 adult medical ICU patientsextubated for less than 2 h and in whom a prophylactic post-extubation NIV was indicated.Patients were randomly mechanically ventilated for 10 min with: PSV without NIV algorithm(PSV-NIV-), PSV with NIV algorithm (PSV-NIV+),NAVAwithout NIV algorithm (NAVANIV-)and NAVA with NIV algorithm (NAVA-NIV+). Breathing pattern descriptors, diaphragmelectrical activity, leaks volume, inspiratory trigger delay (Tdinsp), inspiratory time inexcess (Tiexcess) and the five main asynchronies were quantified. Asynchrony index (AI) andasynchrony index influenced by leaks (AIleaks) were computed.RESULTS. Peak inspiratory pressure and diaphragm electrical activity were similar in thefour conditions. With both PSV and NAVA, NIV algorithm significantly reduced the level ofleak (p\0.01). Tdinsp was not affected by NIV algorithm but was shorter in NAVA than inPSV (p\0.01). Tiexcess was shorter in NAVA and PSV-NIV+ than in PSV-NIV- (p\0.05).The prevalence of double triggering was significantly lower in PSV-NIV+ than in NAVANIV+.As compared to PSV,NAVAsignificantly reduced the prevalence of premature cyclingand late cycling while NIV algorithm did not influenced premature cycling. AI was not affectedby NIV algorithm but was significantly lower in NAVA than in PSV (p\0.05). AIleaks wasquasi null with NAVA and significantly lower than in PSV (p\0.05).CONCLUSIONS. NAVA is feasible in patients receiving a post-extubation prophylacticNIV. NAVA and NIV improve patient-ventilator synchrony in different manners. NAVANIV+offers the best patient-ventilator interaction. Clinical studies are required to assess thepotential clinical benefit of NAVA in patients receiving NIV.

Decision-support tools to manage drug incompatibilities: evaluation by nurses

Introduction Preventing drug incompatibilities has a high impact onthe safety of drug therapy. Although there are no internationalguidelines to manage drug incompatibilities, different decision-supporttools such as handbooks, cross-tables and databases are available.In a previous study, two decision-support tools have been pre-selectedby pharmacists as fitting nurses' needs on the wards1. The objective ofthis study was to have these both tools evaluated by nurses todetermine which would be the most suitable for their daily practice.Materials & Methods Evaluated tools were:1. Cross-table of drug pairs (http://files.chuv.ch/internet-docs/pha/medicaments/pha_phatab_compatibilitessip.pdf)2. Colour-table (a colour for each drug according to the pH: red =acid; blue = basic; yellow = neutral; black = to be infused alone)2Tools were assessed by 48 nurses in 5 units (PICU, adult andgeriatric intensive care, surgery, onco-hematology) using a standardizedform1. The scientific accuracy of the tools was evaluated bydetermining the compatibility of five drugs pairs (rate of correctanswers according to the Trissel's Handbook on Injectable Drugs,chi-square test). Their ergonomics, design, reliability and applicabilitywere estimated using visual analogue scales (VAS 0-10; 0 =null, 10 = excellent). Results are expressed as the median and interquartilerange (IQR) for 25% and 75% (Wilcoxon rank sum test).Results The rate of correct answers was above 90% for both tools(cross-table 96.2% vs colour-table 92.5%, p[0.05).The ergonomics and the applicability were higher for the crosstable[7.1 (IQR25 4.0, IQR75 8.0) vs 5.0 (IQR25 2.7, IQR75 7.0), p =0.025 resp. 8.3 (IQR25 7.4, IQR75 9.2) vs 7.6 (IQR25 5.9, IQR75 8.8)p = 0.047].The design of the colour-table was judged better [4.6 (IQR25 2.9,IQR75 7.1) vs 7.1 (IQR25 5.4, IQR75 8.4) p = 0.002].No difference was observed in terms of reliability [7.3 (IQR25 6.5,IQR75 8.4) vs 6.7 (IQR25 5.0, IQR758.6) p[0.05].The cross-table was globally preferred by 65% of the nurses (27%colour-table, 8% undetermined) and 68% would like to have thisdecision-support tool available for their daily practice.Discussion & Conclusion Both tools showed the same accuracy toassess drug compatibility. In terms of ergonomics and applicabilitythe cross-table was better than the colour-table, and was preferred bythe nurses for their daily practice. The cross-table will be implementedin our hospital as decision-support tool to help nurses tomanage drug incompatibilities.

Body fluid and tissue analysis using filter paper sampling support prior to LC-MS/MS: application to fatal overdose with colchicine

Because of the various matrices available for forensic investigations, the development of versatile analytical approaches allowing the simultaneous determination of drugs is challenging. The aim of this work was to assess a liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform allowing the rapid quantification of colchicine in body fluids and tissues collected in the context of a fatal overdose. For this purpose, filter paper was used as a sampling support and was associated with an automated 96-well plate extraction performed by the LC autosampler itself. The developed method features a 7-min total run time including automated filter paper extraction (2 min) and chromatographic separation (5 min). The sample preparation was reduced to a minimum regardless of the matrix analyzed. This platform was fully validated for dried blood spots (DBS) in the toxic concentration range of colchicine. The DBS calibration curve was applied successfully to quantification in all other matrices (body fluids and tissues) except for bile, where an excessive matrix effect was found. The distribution of colchicine for a fatal overdose case was reported as follows: peripheral blood, 29 ng/ml; urine, 94 ng/ml; vitreous humour and cerebrospinal fluid, < 5 ng/ml; pericardial fluid, 14 ng/ml; brain, < 5 pg/mg; heart, 121 pg/mg; kidney, 245 pg/mg; and liver, 143 pg/mg. Although filter paper is usually employed for DBS, we report here the extension of this alternative sampling support to the analysis of other body fluids and tissues. The developed platform represents a rapid and versatile approach for drug determination in multiple forensic media.

From Support to Control : A Configurational Perspective on Conjugal Quality

The topic of conjugal quality provides an empirical illustration of the relevance of the configurational perspective on families. Based on a longitudinal sample of 1,534 couples living in Switzerland drawn from the study "Social Stratification, Cohesion and Conflict in Contemporary Families", we show that various types of interdependencies with relatives and friends promote distinct conflict management strategies for couples as well as unequal levels of conjugal quality. We find that configurations characterized by supportive and non-interfering relationships with relatives and friends for both partners are associated with higher conjugal quality, while configurations characterized by interference are associated with lower conjugal quality.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.